In this paper, we present ElfCore, a 28nm digital spiking neural network processor tailored for event-driven sensory signal processing. ElfCore is the first to efficiently integrate: (1) a local online self-supervised learning engine that enables multi-layer temporal learning without labeled inputs; (2) a dynamic structured sparse training engine that supports high-accuracy sparse-to-sparse learning; and (3) an activity-dependent sparse weight update mechanism that selectively updates weights based solely on input activity and network dynamics. Demonstrated on tasks including gesture recognition, speech, and biomedical signal processing, ElfCore outperforms state-of-the-art solutions with up to 16X lower power consumption, 3.8X reduced on-chip memory requirements, and 5.9X greater network capacity efficiency.

翻译：本文介绍ElfCore，一款专为事件驱动型传感信号处理设计的28纳米数字脉冲神经网络处理器。ElfCore首次高效整合了以下三大特性：(1) 本地在线自监督学习引擎，无需标注输入即可实现多层时序学习；(2) 动态结构化稀疏训练引擎，支持高精度稀疏到稀疏学习；(3) 基于活动的稀疏权重更新机制，仅依据输入活动性与网络动态特性选择性地更新权重。在包含手势识别、语音及生物医学信号处理等任务上的实验表明，ElfCore相比现有最优方案功耗降低最高达16倍，片上存储需求减少3.8倍，网络容量效率提升5.9倍。